Monthly runoff forecasting via an improved extreme learning machine

Generally, monthly runoff prediction is of great importance for effective water resource planning and management. Extreme learning machine (ELM) is a novel training tool for the famous single layer feed-forward neural network. Due to the satisfying performance, ELM is chosen for monthly runoff forec...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2020-03, Vol.794 (1), p.12017
Main Authors: Niu, Wen-jing, Chen, Yu-bin, Min, Yao-wu, Li, Yu-rong, Zhang, Xiao, Feng, Zhong-kai
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Economic forecasting
Evolutionary algorithms
Generalized inverse
Inverse method
Machine learning
Neural networks
Parameters
Runoff
Water resources
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Summary:Generally, monthly runoff prediction is of great importance for effective water resource planning and management. Extreme learning machine (ELM) is a novel training tool for the famous single layer feed-forward neural network. Due to the satisfying performance, ELM is chosen for monthly runoff forecasting in this research. Nevertheless, it is unfortunately found that ELM easily falls into local optima in practice because the randomly-determined computational parameters remain unchanged during the learning process. Thus, this paper tries to develop an improved extreme learning algorithm (IELM) where the evolutionary algorithm is used to search for satisfying computational parameters while the Moore-Penrose generalized inverse method is used to determine the output weights. The IELM method is applied to forecast the monthly runoff of Hongjiadu reservoir in southwest China. The results show that the proposed method outperforms several traditional algorithms with respect to the performance indicators. Thus, this paper provides a new and effective artificial intelligence approach for the monthly runoff forecasting.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/794/1/012017